Accession Number : ADA112469
Title : Multi-Sample Cluster Analysis Using Akaike's Information Criterion.
Descriptive Note : Technical rept.
Corporate Author : ILLINOIS UNIV AT CHICAGO CIRCLE DEPT OF QUANTITATIVE METHODS
Personal Author(s) : Bozdogan,Hamparsum ; Sclove,Stanley L
PDF Url : ADA112469
Report Date : 30 Jan 1982
Pagination or Media Count : 38
Abstract : Multi-sample cluster analysis, the problem of grouping samples, is studied from an information-theoretic viewpoint via Akaike's Information Criterion (AIC). This criterion combines the maximum value of the likelihood with the number of parameters used in achieving that value. The multi-sample cluster problem is defined, and AIC is developed for this problem. The form of AIC is derived in both univariate and multivariate analysis of variance models. Numerical examples are presented and results are shown to demonstrate the utility of AIC in identifying the best clustering alternatives.
Descriptors : *Clustering, *Multivariate analysis, *Statistical samples, mathematical models, Problem solving, Homogeneity, Comparison, Parameters, Numbers, Maximum likelihood estimation
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE